Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	35,373
2	Florida	32,267
3	Mississippi	32,024
4	Alabama	30,226
5	Arizona	29,655
6	Georgia	27,721
7	South Carolina	27,717
8	Tennessee	27,107
9	Iowa	26,621
10	Arkansas	26,194
11	Texas	26,154
12	North Dakota	25,528
13	Nevada	25,108
14	New York	23,520
15	Rhode Island	22,948
16	New Jersey	22,893
17	South Dakota	22,717
18	Illinois	22,460
19	Idaho	22,129
20	Nebraska	22,089
21	District of Columbia	21,404
22	Utah	20,950
23	Oklahoma	20,531
24	Delaware	20,484
25	California	20,289
26	Maryland	20,228
27	Missouri	19,738
28	Wisconsin	19,707
29	Kansas	19,370
30	North Carolina	18,996
31	Massachusetts	18,714
32	Indiana	17,302
33	Virginia	16,811
34	Minnesota	16,501
35	Connecticut	15,839
36	Kentucky	15,271
37	Puerto Rico	13,727
38	New Mexico	13,460
39	Michigan	13,257
40	Ohio	12,639
41	Pennsylvania	12,359
42	Colorado	11,763
43	Washington	11,634
44	Alaska	10,956
45	Montana	10,648
46	Wyoming	9,166
47	Hawaii	8,410
48	West Virginia	8,205
49	Oregon	7,555
50	New Hampshire	5,915
51	Maine	3,879
52	Vermont	2,762

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	527
2	South Dakota	462
3	Wisconsin	348
4	Iowa	315
5	Arkansas	296
6	South Carolina	296
7	Utah	287
8	Oklahoma	281
9	Montana	271
10	Idaho	251
11	Missouri	248
12	Nebraska	231
13	Wyoming	207
14	Mississippi	196
15	Kentucky	173
16	Kansas	170
17	Tennessee	165
18	Alabama	164
19	Texas	156
20	Illinois	146
21	Puerto Rico	142
22	Louisiana	139
23	Georgia	136
24	Nevada	131
25	North Carolina	131
26	Minnesota	122
27	Rhode Island	119
28	Florida	117
29	Indiana	110
30	Alaska	109
31	Colorado	104
32	West Virginia	99
33	Delaware	95
34	Michigan	91
35	Virginia	91
36	New Mexico	86
37	California	79
38	Washington	78
39	Hawaii	74
40	Arizona	73
41	Ohio	73
42	Oregon	68
43	Maryland	67
44	Pennsylvania	64
45	District of Columbia	60
46	Massachusetts	57
47	New Jersey	52
48	Connecticut	41
49	New York	40
50	Maine	27
51	New Hampshire	22
52	Vermont	2

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,811
2	New York	1,680
3	Massachusetts	1,358
4	Connecticut	1,261
5	Louisiana	1,166
6	Rhode Island	1,044
7	Mississippi	965
8	District of Columbia	879
9	Arizona	763
10	Michigan	702
11	Illinois	693
12	Delaware	646
13	Maryland	646
14	Florida	642
15	Pennsylvania	636
16	South Carolina	636
17	Georgia	627
18	Texas	537
19	Indiana	527
20	Alabama	511
21	Nevada	508
22	Arkansas	412
23	Iowa	412
24	New Mexico	409
25	Ohio	403
26	California	389
27	Virginia	364
28	Minnesota	361
29	Colorado	354
30	Tennessee	334
31	Missouri	333
32	New Hampshire	322
33	North Carolina	322
34	Washington	285
35	North Dakota	282
36	Kentucky	260
37	Idaho	256
38	Nebraska	247
39	Oklahoma	247
40	South Dakota	237
41	Wisconsin	218
42	Kansas	217
43	Puerto Rico	196
44	West Virginia	181
45	Montana	154
46	Utah	138
47	Oregon	128
48	Maine	104
49	Vermont	92
50	Hawaii	86
51	Wyoming	86
52	Alaska	58

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	8
2	Florida	7
3	Mississippi	7
4	Georgia	6
5	Missouri	6
6	Arkansas	5
7	Alabama	4
8	South Carolina	4
9	Texas	4
10	Arizona	3
11	Kansas	3
12	Louisiana	3
13	Nevada	3
14	North Carolina	3
15	South Dakota	3
16	Tennessee	3
17	Virginia	3
18	California	2
19	Idaho	2
20	Iowa	2
21	Massachusetts	2
22	Nebraska	2
23	Ohio	2
24	Oklahoma	2
25	Rhode Island	2
26	West Virginia	2
27	Delaware	1
28	Illinois	1
29	Indiana	1
30	Kentucky	1
31	Maryland	1
32	Michigan	1
33	Minnesota	1
34	Montana	1
35	New Mexico	1
36	Pennsylvania	1
37	Puerto Rico	1
38	Washington	1
39	Wisconsin	1
40	Alaska	0
41	Colorado	0
42	Connecticut	0
43	District of Columbia	0
44	Hawaii	0
45	Maine	0
46	New Hampshire	0
47	New Jersey	0
48	New York	0
49	Oregon	0
50	Utah	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	149,570	1	99
Chattahoochee	Georgia	149,354	2	99
Lafayette	Florida	146,521	3	99
Trousdale	Tennessee	146,313	4	99
Lake	Tennessee	130,274	5	99
Davidson	Tennessee	40,902	192	93
Richland	South Carolina	35,677	270	91
York	South Carolina	18,660	1129	64
Orange	California	17,011	1284	59
Pierce	Washington	9,527	2170	30

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,966	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,131	5	99
Richland	South Carolina	563	698	77
Davidson	Tennessee	429	971	69
Orange	California	372	1108	64
York	South Carolina	249	1461	53
Pierce	Washington	243	1479	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.